wv4_est_weighted <- readRDS(here("models", "Wave 4",
"Weighted Bayesian", "wv4_weighted_est.RDS"))
wv7_est_weighted <- readRDS(here("models", "Wave 7",
"Weighted Bayesian", "wv7_weighted_est.RDS"))
obs_wv4_weighted <- readRDS(here("models", "Wave 4",
"Weighted Bayesian", "obs_wv4_weighted.RDS"))
obs_wv7_weighted <- readRDS(here("models", "Wave 7",
"Weighted Bayesian", "obs_wv7_weighted.RDS"))
# Wave 4
wv4_est_weighted <- left_join(wv4_est_weighted, obs_wv4_weighted, by = "strata")
wv4_est_weighted <- wv4_est_weighted %>%
mutate(wave = 4) %>%
mutate(est_obs_dif = eprod_edsd - mean) %>%
mutate(mean = mean*100,
q5 = q5*100,
q95 = q95*100,
eprod_edsd = eprod_edsd*100,
est_obs_dif = est_obs_dif*100)
# Wave 7
wv7_est_weighted <- left_join(wv7_est_weighted, obs_wv7_weighted, by = "strata")
wv7_est_weighted <- wv7_est_weighted %>%
mutate(wave = 7) %>%
mutate(est_obs_dif = eprod_edsd - mean) %>%
mutate(mean = mean*100,
q5 = q5*100,
q95 = q95*100,
eprod_edsd = eprod_edsd*100,
est_obs_dif = est_obs_dif*100)
# Dataframe with both waves, rows
est_weighted <- rbind.data.frame(wv4_est_weighted, wv7_est_weighted)
# Dataframe with both waves, columns
est_weighted_wide <- est_weighted %>%
pivot_wider(
id_cols = c(strata, strata_label),
names_from = wave,
values_from = c(mean, q5, q95, eprod_edsd),
names_glue = "{.value}_wave{wave}") %>%
mutate(est_wave_diff = mean_wave7-mean_wave4)
wv4_est_weighted %>%
ggplot(aes(y = mean, x = reorder(strata_label, mean))) +
geom_pointrange(aes(ymin = q5, ymax = q95)) +
ylab("Predicted EDSD E-Product Use (95% Crl)") +
xlab("Stratum Rank") +
coord_flip() +
theme_bw()
wv4_est_weighted %>%
mutate(region = word(strata_label, 1, sep = fixed(","))) %>%
ggplot(aes(y = mean, x = reorder(strata_label, mean))) +
geom_pointrange(aes(ymin = q5, ymax = q95)) +
ylab("Predicted EDSD E-Product Use (95% Crl)") +
xlab("Stratum") +
coord_flip() +
theme_bw() +
facet_wrap(~ region, scales = "free_y", ncol = 1)
wv4_est_weighted %>%
mutate(race_ethnicity = word(strata_label, 2, sep = fixed(","))) %>%
ggplot(aes(y = mean, x = reorder(strata_label, mean))) +
geom_pointrange(aes(ymin = q5, ymax = q95)) +
ylab("Predicted EDSD E-Product Use (95% Crl)") +
xlab("Stratum") +
coord_flip() +
theme_bw() +
facet_wrap(~ race_ethnicity, scales = "free_y", ncol = 1)
wv4_est_weighted %>%
mutate(sex = word(strata_label, 3, sep = fixed(","))) %>%
ggplot(aes(y = mean, x = reorder(strata_label, mean))) +
geom_pointrange(aes(ymin = q5, ymax = q95)) +
ylab("Predicted EDSD E-Product Use (95% Crl)") +
xlab("Stratum") +
coord_flip() +
theme_bw() +
facet_wrap(~ sex, scales = "free_y", ncol = 1)
wv4_est_weighted %>%
mutate(age = word(strata_label, 4, sep = fixed(","))) %>%
ggplot(aes(y = mean, x = reorder(strata_label, mean))) +
geom_pointrange(aes(ymin = q5, ymax = q95)) +
ylab("Predicted EDSD E-Product Use (95% Crl)") +
xlab("Stratum") +
coord_flip() +
theme_bw() +
facet_wrap(~ age, scales = "free_y", ncol = 1)
On average, the mean predicted EDSD rate was only 0.14 percentage points low than the weighted, observed rate (5.44% vs. 5.47%).
wv4_est_weighted %>%
ggplot(aes(x = reorder(strata_label, abs(est_obs_dif)))) +
geom_point(aes(y = mean, color = "Predicted"), size = 2) +
geom_point(aes(y = eprod_edsd, color = "Observed"), size = 2) +
scale_color_manual(values = c("Predicted" = "black", "Observed" = "purple")) +
ylab("Percent EDSD E-Product Use") +
xlab("Stratum (Ranked by Accuracy, Lowest to Highest)") +
coord_flip() +
theme_bw() +
theme(legend.title = element_blank())
describe(wv4_est_weighted$eprod_edsd)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 96 5.47 4.37 4.45 4.79 3.52 0 22.02 22.02 1.44 1.94 0.45
describe(wv4_est_weighted$mean)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 96 5.33 3.58 4.89 4.91 3.94 1.25 16.4 15.15 1.06 0.85 0.37
wv7_est_weighted %>%
ggplot(aes(y = mean, x = reorder(strata_label, mean))) +
geom_pointrange(aes(ymin = q5, ymax = q95)) +
ylab("Predicted Percent EDSD E-Product User") +
xlab("Stratum Rank") +
coord_flip() +
theme_bw()
wv7_est_weighted %>%
mutate(region = word(strata_label, 1, sep = fixed(","))) %>%
ggplot(aes(y = mean, x = reorder(strata_label, mean))) +
geom_pointrange(aes(ymin = q5, ymax = q95)) +
ylab("Predicted Percent EDSD E-Product User") +
xlab("Stratum") +
coord_flip() +
theme_bw() +
facet_wrap(~ region, scales = "free_y", ncol = 1)
wv7_est_weighted %>%
mutate(race_ethnicity = word(strata_label, 2, sep = fixed(","))) %>%
ggplot(aes(y = mean, x = reorder(strata_label, mean))) +
geom_pointrange(aes(ymin = q5, ymax = q95)) +
ylab("Predicted Percent EDSD E-Product User") +
xlab("Stratum") +
coord_flip() +
theme_bw() +
facet_wrap(~ race_ethnicity, scales = "free_y", ncol = 1)
wv7_est_weighted %>%
mutate(sex = word(strata_label, 3, sep = fixed(","))) %>%
ggplot(aes(y = mean, x = reorder(strata_label, mean))) +
geom_pointrange(aes(ymin = q5, ymax = q95)) +
ylab("Predicted Percent EDSD E-Product User") +
xlab("Stratum") +
coord_flip() +
theme_bw() +
facet_wrap(~ sex, scales = "free_y", ncol = 1)
wv7_est_weighted %>%
mutate(age = word(strata_label, 4, sep = fixed(","))) %>%
ggplot(aes(y = mean, x = reorder(strata_label, mean))) +
geom_pointrange(aes(ymin = q5, ymax = q95)) +
ylab("Predicted Percent EDSD E-Product User") +
xlab("Stratum") +
coord_flip() +
theme_bw() +
facet_wrap(~ age, scales = "free_y", ncol = 1)
On average, the mean predicted EDSD rate was only 0.17 percentage points higher than the weighted, observed rate (9.67% vs. 9.5%).
wv7_est_weighted %>%
ggplot(aes(x = reorder(strata_label, abs(est_obs_dif)))) +
geom_point(aes(y = mean, color = "Predicted"), size = 2) +
geom_point(aes(y = eprod_edsd, color = "Observed"), size = 2) +
scale_color_manual(values = c("Predicted" = "black", "Observed" = "purple")) +
ylab("Predicted EDSD E-Product Use Rates") +
xlab("Stratum (Ranked by Accuracy, Lowest to Highest)") +
coord_flip() +
theme_bw() +
theme(legend.title = element_blank())
describe(wv7_est_weighted$eprod_edsd)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 96 9.67 7.1 8.61 9.14 8.19 0 24.71 24.71 0.53 -0.9 0.72
describe(wv7_est_weighted$mean)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 96 9.5 6.61 8.98 9.01 9.6 1.19 23.65 22.46 0.37 -1 0.67
est_weighted_wide %>%
ggplot(aes(x = reorder(strata_label, est_wave_diff))) +
# Wave 4 point + interval
geom_point(aes(y = mean_wave4, color = "Wave 4"), size = 2) +
geom_pointrange(aes(y = mean_wave4, ymin = q5_wave4, ymax = q95_wave4), color = "turquoise4") +
# Wave 7 point + interval
geom_point(aes(y = mean_wave7, color = "Wave 7"), size = 2) +
geom_pointrange(aes(y = mean_wave7, ymin = q5_wave7, ymax = q95_wave7), color = "darkorchid4") +
scale_color_manual(values = c("Wave 4" = "turquoise4", "Wave 7" = "darkorchid4")) +
ylab("Predicted EDSD E-Product Use (95% Crl)") +
xlab("Stratum (Ranked by Change, Greatest to Least)") +
coord_flip() +
theme_bw() +
theme(legend.title = element_blank())
region_facet <- est_weighted_wide %>%
mutate(region = word(strata_label, 1, sep = fixed(","))) %>%
ggplot(aes(x = reorder(strata_label, est_wave_diff))) +
geom_point(aes(y = mean_wave4, color = "Wave 4"), size = 2) +
geom_pointrange(aes(y = mean_wave4, ymin = q5_wave4, ymax = q95_wave4), color = "turquoise4") +
geom_point(aes(y = mean_wave7, color = "Wave 7"), size = 2) +
geom_pointrange(aes(y = mean_wave7, ymin = q5_wave7, ymax = q95_wave7), color = "darkorchid4") +
scale_color_manual(values = c("Wave 4" = "turquoise4", "Wave 7" = "darkorchid4")) +
ylab("Predicted EDSD E-Product Use (95% Crl)") +
xlab("Stratum (Ranked by Change, Greatest to Least)") +
coord_flip() +
facet_wrap(~ region, scales = "free_y", ncol = 1) +
theme_bw() +
theme(legend.title = element_blank())
region_facet
est_weighted_wide %>%
mutate(race_ethnicity = word(strata_label, 2, sep = fixed(","))) %>%
ggplot(aes(x = reorder(strata_label, est_wave_diff))) +
geom_point(aes(y = mean_wave4, color = "Wave 4"), size = 2) +
geom_pointrange(aes(y = mean_wave4, ymin = q5_wave4, ymax = q95_wave4), color = "turquoise4") +
geom_point(aes(y = mean_wave7, color = "Wave 7"), size = 2) +
geom_pointrange(aes(y = mean_wave7, ymin = q5_wave7, ymax = q95_wave7), color = "darkorchid4") +
scale_color_manual(values = c("Wave 4" = "turquoise4", "Wave 7" = "darkorchid4")) +
ylab("Predicted EDSD E-Product Use (95% Crl)") +
xlab("Stratum (Ranked by Change, Greatest to Least)") +
coord_flip() +
facet_wrap(~ race_ethnicity, scales = "free_y", ncol = 1) +
theme_bw() +
theme(legend.title = element_blank())
est_weighted_wide %>%
mutate(sex = word(strata_label, 3, sep = fixed(","))) %>%
ggplot(aes(x = reorder(strata_label, est_wave_diff))) +
geom_point(aes(y = mean_wave4, color = "Wave 4"), size = 2) +
geom_pointrange(aes(y = mean_wave4, ymin = q5_wave4, ymax = q95_wave4), color = "turquoise4") +
geom_point(aes(y = mean_wave7, color = "Wave 7"), size = 2) +
geom_pointrange(aes(y = mean_wave7, ymin = q5_wave7, ymax = q95_wave7), color = "darkorchid4") +
scale_color_manual(values = c("Wave 4" = "turquoise4", "Wave 7" = "darkorchid4")) +
ylab("Predicted EDSD E-Product Use (95% Crl)") +
xlab("Stratum (Ranked by Change, Greatest to Least)") +
coord_flip() +
facet_wrap(~ sex, scales = "free_y", ncol = 1) +
theme_bw() +
theme(legend.title = element_blank())
age_facet <- est_weighted_wide %>%
mutate(age = word(strata_label, 4, sep = fixed(","))) %>%
ggplot(aes(x = reorder(strata_label, est_wave_diff))) +
geom_point(aes(y = mean_wave4, color = "Wave 4"), size = 2) +
geom_pointrange(aes(y = mean_wave4, ymin = q5_wave4, ymax = q95_wave4), color = "turquoise4") +
geom_point(aes(y = mean_wave7, color = "Wave 7"), size = 2) +
geom_pointrange(aes(y = mean_wave7, ymin = q5_wave7, ymax = q95_wave7), color = "darkorchid4") +
scale_color_manual(values = c("Wave 4" = "turquoise4", "Wave 7" = "darkorchid4")) +
ylab("Predicted EDSD E-Product Use (95% Crl)") +
xlab("Stratum (Ranked by Change, Greatest to Least)") +
coord_flip() +
facet_wrap(~ age, scales = "free_y", ncol = 1) +
theme_bw() +
theme(legend.title = element_blank())
age_facet
age_sex_facet <- est_weighted_wide %>%
mutate(age = word(strata_label, 4, sep = fixed(",")),
sex = word(strata_label, 3, sep = fixed(","))) %>%
ggplot(aes(x = reorder(strata_label, est_wave_diff))) +
geom_point(aes(y = mean_wave4, color = "Wave 4"), size = 2) +
geom_pointrange(aes(y = mean_wave4, ymin = q5_wave4, ymax = q95_wave4), color = "turquoise4") +
geom_point(aes(y = mean_wave7, color = "Wave 7"), size = 2) +
geom_pointrange(aes(y = mean_wave7, ymin = q5_wave7, ymax = q95_wave7), color = "darkorchid4") +
scale_color_manual(values = c("Wave 4" = "turquoise4", "Wave 7" = "darkorchid4")) +
ylab("Predicted EDSD E-Product Use (95% Crl)") +
xlab("Stratum (Ranked by Change, Greatest to Least)") +
coord_flip() +
facet_wrap(~ interaction(age, sex), scales = "free_y") +
theme_bw() +
theme(legend.title = element_blank())
age_sex_facet